{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2203007e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5488a324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'boxplot')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "tang_data = [np.random.normal(0,std,100) for std in range(1,4)]\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "plt.boxplot(tang_data,notch=False,sym='s',vert=True)\n",
    "plt.xticks([y+1 for y in range(len(tang_data))],['x1','x2','x3'])\n",
    "plt.xlabel('x')\n",
    "plt.title('boxplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e86bb21e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
